Snake, scorpion, and spider venom are most frequently associated with poisonous bites, but with the help of artificial intelligence, they might be able to help fight antibiotic resistance, which contributes to more than one million deaths worldwide each year.
In a study published in Nature Communications, researchers at the University of Pennsylvania used a deep-learning system called APEX to sift through a database of more than 40 million venom encrypted peptides (VEPs), tiny proteins evolved by animals for attack or as a defense mechanism. In a matter of hours, the algorithm flagged 386 compounds with the molecular hallmarks of next-generation antibiotics.
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“Venoms are evolutionary masterpieces, yet their antimicrobial potential has barely been explored,” said senior author César de la Fuente, PhD, a Presidential Associate Professor of Psychiatry, Microbiology, Bioengineering, Chemical and Biomolecular Engineering, and Chemistry. “APEX lets us scan an immense chemical space in just hours and identify peptides with exceptional potential to fight the world’s most stubborn pathogens.”
Combining emerging tech with established methods
From the AI-selected shortlist, the team synthesized 58 venom peptides for laboratory testing. 53 killed drug-resistant bacteria—including Escherichia coli and Staphylococcus aureus—at doses that were harmless to human red blood cells.
“By pairing computational triage with traditional lab experimentation, we delivered one of the most comprehensive investigations of venom derived antibiotics to date,” added co-author Marcelo Torres, PhD, a research associate at Penn. Changge Guan, PhD, a postdoctoral researcher in the De la Fuente Lab and co-author, noted that the platform mapped more than 2,000 entirely new antibacterial motifs—short, specific sequences of amino acids within a protein or peptide responsible for their ability to kill or inhibit bacterial growth.
The team is now taking the top peptide candidates which could lead to new antibiotics and improving them through medicinal-chemistry tweaks.
Background
Support included funding from the Procter & Gamble Company, United Therapeutics, a BBRF Young Investigator Grant, the Nemirovsky Prize, Penn Health-Tech Accelerator Award, Defense Threat Reduction Agency grants HDTRA11810041 and HDTRA1-23-1-0001, and the Dean’s Innovation Fund from the Perelman School of Medicine at the University of Pennsylvania. Research reported in this publication was supported by the Langer Prize (AIChE Foundation), the NIH R35GM138201, and DTRA HDTRA1-21-1-0014.
Cesar de la Fuente provides consulting services to Invaio Sciences and is a member of the Scientific Advisory Boards of Nowture S.L. and Phare Bio. The de la Fuente Lab has received research funding or in-kind donations from United Therapeutics, Strata Manufacturing PJSC, and Procter & Gamble, none of which were used in support of this work. An invention disclosure associated with this work has been filed.
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